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31eb9f800c
@ -27,7 +27,7 @@
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# "close_outstanding_positions": false,
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "22:30:00",
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"end_session": "19:00:00",
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"timezone": "America/New_York"
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}
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}
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@ -570,12 +570,9 @@ class BacktestResult:
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symbol_trades = [trade for trade in trades if trade["symbol"] == symbol]
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# Calculate returns for all trade combinations
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for idx in range(0, len(symbol_trades), 2):
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trade1 = trades[idx]
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trade2 = trades[idx + 1]
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assert trade1["timestamp"] < trade2["timestamp"], f"Trade 1: {trade1['timestamp']} is not less than Trade 2: {trade2['timestamp']}"
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assert trade1["action"] == "OPEN" and trade2["action"] == "CLOSE", f"Trade 1: {trade1['action']} and Trade 2: {trade2['action']} are the same"
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for i in range(len(symbol_trades) - 1):
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trade1 = trades[i]
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trade2 = trades[i + 1]
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# Calculate return based on action combination
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trade_return = 0
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@ -592,8 +589,6 @@ class BacktestResult:
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pair_trades.append(
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(
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symbol,
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trade1["timestamp"],
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trade2["timestamp"],
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trade1["side"],
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trade1["price"],
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trade2["side"],
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@ -601,7 +596,7 @@ class BacktestResult:
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trade_return,
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trade1["scaled_disequilibrium"],
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trade2["scaled_disequilibrium"],
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f"{idx + 1}", # Trade sequence number
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i + 1, # Trade sequence number
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)
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)
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@ -612,8 +607,6 @@ class BacktestResult:
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print(f" {pair}:")
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for (
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symbol,
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trade1["timestamp"],
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trade2["timestamp"],
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trade1["side"],
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trade1["price"],
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trade2["side"],
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@ -632,14 +625,13 @@ class BacktestResult:
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f" Close Dis-eq: {trade2["scaled_disequilibrium"]:.2f}"
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print(
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f" {trade2['timestamp'].time()} {symbol} (Trade #{trade_num}):"
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f" {symbol} (Trade #{trade_num}):"
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f" {trade1["side"]} @ ${trade1["price"]:.2f},"
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f" {trade2["side"]} @ ${trade2["price"]:.2f},"
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f" Return: {trade_return:.2f}%{disequil_info}"
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)
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print(f" Pair Total Return: {pair_return:.2f}%")
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day_return += pair_return
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# Print day total return and add to global realized PnL
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if day_return != 0:
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@ -9,18 +9,16 @@ from pt_trading.trading_pair import PairState, TradingPair
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from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
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NanoPerMin = 1e9
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class RollingFit(PairsTradingFitMethod):
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"""
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'''
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N O T E:
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=========
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- This class remains to be abstract
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- The following methods are to be implemented in the subclass:
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- create_trading_pair()
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=========
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"""
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'''
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def __init__(self) -> None:
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super().__init__()
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@ -35,18 +33,16 @@ class RollingFit(PairsTradingFitMethod):
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pair.user_data_["state"] = PairState.INITIAL
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# Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
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pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype(
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{
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"time": "datetime64[ns]",
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"symbol": "string",
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"side": "string",
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"action": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object",
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}
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)
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pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype({
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"time": "datetime64[ns]",
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"symbol": "string",
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"side": "string",
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"action": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object"
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})
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training_minutes = config["training_minutes"]
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curr_predicted_row_idx = 0
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@ -70,9 +66,7 @@ class RollingFit(PairsTradingFitMethod):
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# ================================ PREDICTION ================================
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self.pair_predict_result_ = pair.predict()
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except Exception as e:
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raise RuntimeError(
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f"{pair}: TrainingPrediction failed: {str(e)}"
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) from e
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raise RuntimeError(f"{pair}: TrainingPrediction failed: {str(e)}") from e
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# break
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@ -82,8 +76,8 @@ class RollingFit(PairsTradingFitMethod):
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curr_predicted_row_idx += 1
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self._create_trading_signals(pair, config, bt_result)
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print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
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print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
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return pair.get_trades()
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def _create_trading_signals(
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@ -99,14 +93,8 @@ class RollingFit(PairsTradingFitMethod):
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pred_row = predicted_df.iloc[curr_predicted_row_idx]
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scaled_disequilibrium = pred_row["scaled_disequilibrium"]
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if pair.user_data_["state"] in [
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PairState.INITIAL,
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PairState.CLOSE,
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PairState.CLOSE_POSITION,
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PairState.CLOSE_STOP_LOSS,
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PairState.CLOSE_STOP_PROFIT,
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]:
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if scaled_disequilibrium >= open_threshold:
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if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSE, PairState.CLOSE_POSITION]:
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if scaled_disequilibrium >= open_threshold:
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open_trades = self._get_open_trades(
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pair, row=pred_row, open_threshold=open_threshold
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)
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@ -133,21 +121,23 @@ class RollingFit(PairsTradingFitMethod):
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pair, row=pred_row, close_threshold=close_threshold
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)
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if close_trades is not None:
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close_trades["status"] = pair.user_data_[
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"stop_close_state"
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].name
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close_trades["status"] = pair.user_data_["stop_close_state"].name
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print(f"STOP CLOSE TRADES:\n{close_trades}")
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pair.add_trades(close_trades)
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pair.user_data_["state"] = pair.user_data_["stop_close_state"]
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pair.user_data_["state"] = pair.user_data_["stop_close_state"]
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pair.on_close_trades(close_trades)
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# Outstanding positions
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if pair.user_data_["state"] == PairState.OPEN:
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print(f"{pair}: *** Position is NOT CLOSED. ***")
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print(
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f"{pair}: *** Position is NOT CLOSED. ***"
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)
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# outstanding positions
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if config["close_outstanding_positions"]:
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close_position_trades = self._get_close_trades(
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pair=pair, row=pred_row, close_threshold=close_threshold
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pair=pair,
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row=pred_row,
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close_threshold=close_threshold
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)
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if close_position_trades is not None:
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close_position_trades["status"] = PairState.CLOSE_POSITION.name
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@ -233,17 +223,15 @@ class RollingFit(PairsTradingFitMethod):
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columns=self.TRADES_COLUMNS,
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)
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# Ensure consistent dtypes
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return df.astype(
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{
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object",
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}
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)
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return df.astype({
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object"
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})
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def _get_close_trades(
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self, pair: TradingPair, row: pd.Series, close_threshold: float
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@ -289,17 +277,16 @@ class RollingFit(PairsTradingFitMethod):
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columns=self.TRADES_COLUMNS,
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)
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# Ensure consistent dtypes
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return df.astype(
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{
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object",
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}
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)
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return df.astype({
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object"
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})
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def reset(self) -> None:
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curr_training_start_idx = 0
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